# Import necessary packages
import os
import scanpy as sc
import numpy as np
import pandas as pd
import anndata as ad
from datetime import datetime as dt

# Scanpy settings
sc.settings.verbosity = 3
sc.logging.print_header()

# Set working directory
os.chdir("/home/dalbao/2023-012-Runx3mutD8scRNA/AlbaoRunx3Manuscript/single_cell/02_optimization")

adata = "01_25-12-05-17-25_preprocessing_RPE.h5ad"

2025-12-05 22:40:10.192293: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.

scanpy==1.9.5 anndata==0.10.2 umap==0.5.4 numpy==1.24.4 scipy==1.11.3 pandas==2.1.1 scikit-learn==1.3.1 statsmodels==0.14.0 igraph==0.11.2 louvain==0.8.1 pynndescent==0.5.10

# Determine work location
print("The work location for this notebook is: " + os.getcwd() + "\n")

# Get a timestamp for the start of the run
timestamp = dt.now()
print("This notebook was last run on " + timestamp.strftime("%y-%m-%d %H:%M") + "\n")

The work location for this notebook is: /blue/m.pipkin/dalbao/2023-012-Runx3mutD8scRNA/AlbaoRunx3Manuscript/single_cell/02_optimization

This notebook was last run on 25-12-05 22:40

# List items in outs folder
adata = ad.read_h5ad("../../h5ad/" + adata)

# Inspect AnnData
print(adata)

AnnData object with n_obs × n_vars = 16314 × 18505
    obs: 'experiment', 'group', 'timepoint', 'infection', 'cell_id', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'doublet_score', 'predicted_doublet', 'scDblFinder_score', 'scDblFinder_class', 'gem', 'QC', 'n_genes', 'S_score', 'G2M_score', 'phase'
    var: 'mt', 'ribo', 'n_cells_by_counts-gem1', 'mean_counts-gem1', 'log1p_mean_counts-gem1', 'pct_dropout_by_counts-gem1', 'total_counts-gem1', 'log1p_total_counts-gem1', 'n_cells_by_counts-gem2', 'mean_counts-gem2', 'log1p_mean_counts-gem2', 'pct_dropout_by_counts-gem2', 'total_counts-gem2', 'log1p_total_counts-gem2', 'n_cells', 'n_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'group_colors', 'hvg', 'log1p', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'
    layers: 'counts', 'soupX_counts'

# Define a function to automate the iteration of different neighbor values, holding PC constant.
def iterate_neighbors(adata_local, pcs = 25, neighbors = 10, min = 0, max = 11):
    # Loop over different nearest neighbor values based on provided min and max values
    for index in range(min, max):
        # Calculate nearest neighbors with n_neighbors of neighbors + index
        adata_mod = sc.pp.neighbors(adata_local, n_neighbors = neighbors + index, n_pcs = pcs, copy = True)
        
        # Do leiden clustering from neighborhood graph
        sc.tl.leiden(adata_mod)
        
        # Initialize the PAGA
        sc.tl.paga(adata_mod)
        sc.pl.paga(adata_mod, title = "neighbor " + str(neighbors + index) + " pcs " + str(pcs))

        # Do a UMAP based on initialized PAGA graph
        sc.tl.umap(adata_mod, init_pos='paga')

        # Plot UMAP annotated by leiden clusters and original identity
        sc.pl.umap(adata_mod, color = "group", title = "neighbor " + str(neighbors + index) + " pcs " + str(pcs))
        sc.pl.umap(adata_mod, color = "leiden", title = "neighbor " + str(neighbors + index) + " pcs " + str(pcs))

        # Get leiden clusters and original identities
        obs_df = sc.get.obs_df(adata_mod, ["group", "leiden"])
        # Print tapply
        print(obs_df.groupby("group").value_counts().unstack())

        del(adata_mod) # Cleanup
        
    # End of loop
    
    del(adata_local)
            
# End of function

iterate_neighbors(adata, pcs = 25, neighbors = 3, min = 0, max = 18)

computing neighbors
    using 'X_pca' with n_pcs = 25
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:14)
running Leiden clustering
    finished: found 52 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running PAGA
    finished: added
    'paga/connectivities', connectivities adjacency (adata.uns)
    'paga/connectivities_tree', connectivities subtree (adata.uns) (0:00:01)
--> added 'pos', the PAGA positions (adata.uns['paga'])

computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:03)

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

leiden    0    1    2    3    4    5    6    7    8    9  ...  42  43  44  45  \
group                                                     ...                   
Base    163  326    1  255  189   18   11  297   18  194  ...  46   4   0  12   
Naive     0    0    0    0    0    0    0    1    0    0  ...   0   1   0   0   
Null    586  230    0  270  401    0    0  154   33  248  ...  11  11   0  10   
WT       41  113  657   59   45  620  595   29   24   40  ...   5   8   0  18   
d5        6   25    5   14    8    7    2   12  430   24  ...   0  37  46   0   
d8       49  104    0   64    9    3    1  111   75   64  ...  32  15   0   6   

leiden  46  47  48  49  50  51  
group                           
Base    18  22   0   0   0   0  
Naive    0   0   0   0   3   0  
Null    14   1   0   0   0   0  
WT       5   4   0   0   0   0  
d5       0   0   9   7   0   3  
d8       4   1   0   0   0   0  

[6 rows x 52 columns]
computing neighbors

/scratch/local/20367147/ipykernel_1279247/2632075354.py:25: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  print(obs_df.groupby("group").value_counts().unstack())

    using 'X_pca' with n_pcs = 25
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:01)
running Leiden clustering
    finished: found 24 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:01)
running PAGA
    finished: added
    'paga/connectivities', connectivities adjacency (adata.uns)
    'paga/connectivities_tree', connectivities subtree (adata.uns) (0:00:00)
--> added 'pos', the PAGA positions (adata.uns['paga'])

computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:04)

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

iterate_neighbors(adata, pcs = 30, neighbors = 3, min = 0, max = 18)

computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:01)
running Leiden clustering
    finished: found 50 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running PAGA
    finished: added
    'paga/connectivities', connectivities adjacency (adata.uns)
    'paga/connectivities_tree', connectivities subtree (adata.uns) (0:00:01)
--> added 'pos', the PAGA positions (adata.uns['paga'])

computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:03)

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

leiden    0    1    2    3    4    5    6    7    8    9  ...  40  41  42  43  \
group                                                     ...                   
Base    278  164   24  282  289    2  254   12  287  165  ...  55   0  13  19   
Naive     0    0    0    0    1    0    0    0    0    0  ...   0   0   0   0   
Null    297  517    4  173  162    1  275    1  144   84  ...  19   1  12  14   
WT       76   33  623   59   65  604   38  562   96   24  ...   1   0  28   9   
d5       24    1    7   14   18    6    7    6   16    7  ...   1  68   0   0   
d8      120   28    3  114  102    0   23    0   21  262  ...  19   0   1   4   

leiden  44  45  46  47  48  49  
group                           
Base     0   0   0   0   0   0  
Naive    0   0   0   3   0   0  
Null     0   0   0   0   0   0  
WT       0   0   4   0   3   0  
d5      15   5   0   0   0   3  
d8       0   0   0   0   0   0  

[6 rows x 50 columns]
computing neighbors

/scratch/local/20367147/ipykernel_1279247/2632075354.py:25: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  print(obs_df.groupby("group").value_counts().unstack())

    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:01)
running Leiden clustering
    finished: found 27 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running PAGA
    finished: added
    'paga/connectivities', connectivities adjacency (adata.uns)
    'paga/connectivities_tree', connectivities subtree (adata.uns) (0:00:00)
--> added 'pos', the PAGA positions (adata.uns['paga'])

computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:04)

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

iterate_neighbors(adata, pcs = 35, neighbors = 3, min = 0, max = 18)

computing neighbors
    using 'X_pca' with n_pcs = 35
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:01)
running Leiden clustering
    finished: found 46 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:01)
running PAGA
    finished: added
    'paga/connectivities', connectivities adjacency (adata.uns)
    'paga/connectivities_tree', connectivities subtree (adata.uns) (0:00:01)
--> added 'pos', the PAGA positions (adata.uns['paga'])

computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:04)

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

leiden    0    1    2    3    4    5    6    7    8    9  ...  36  37  38  39  \
group                                                     ...                   
Base      9  251  210  301    4  147   18    3  285  148  ...  79   1   1  14   
Naive     0    0    0    0    0    0    0    0    1    0  ...   0   1   0   0   
Null      1  144  395  228    1  318    1    0  158  340  ...  35  11   0  16   
WT      915   38   43   48  678   49  534  551   76   25  ...   9   7   2  26   
d5        9   15   24   12    0    4    5    3    4    6  ...   1  36  61   0   
d8        2  344   70  103    0   82    3    0   17   17  ...   8  11   1   1   

leiden  40  41  42  43  44  45  
group                           
Base    21   0   0   0   0   0  
Naive    0   0   0   4   3   0  
Null    16   0   0   0   0   0  
WT       6  10   0   0   0   2  
d5       0   0   7   0   0   0  
d8       3   0   0   0   0   1  

[6 rows x 46 columns]
computing neighbors

/scratch/local/20367147/ipykernel_1279247/2632075354.py:25: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  print(obs_df.groupby("group").value_counts().unstack())

    using 'X_pca' with n_pcs = 35
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:01)
running Leiden clustering
    finished: found 25 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:01)
running PAGA
    finished: added
    'paga/connectivities', connectivities adjacency (adata.uns)
    'paga/connectivities_tree', connectivities subtree (adata.uns) (0:00:00)
--> added 'pos', the PAGA positions (adata.uns['paga'])

computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:04)

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

iterate_neighbors(adata, pcs = 40, neighbors = 3, min = 0, max = 18)

computing neighbors
    using 'X_pca' with n_pcs = 40
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:01)
running Leiden clustering
    finished: found 47 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running PAGA
    finished: added
    'paga/connectivities', connectivities adjacency (adata.uns)
    'paga/connectivities_tree', connectivities subtree (adata.uns) (0:00:01)
--> added 'pos', the PAGA positions (adata.uns['paga'])

computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:04)

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

leiden    0    1    2    3    4    5    6    7    8    9  ...  37  38  39  40  \
group                                                     ...                   
Base     39  139  350  246  285    8    0    0    0   19  ...  82  48  77  50   
Naive     0    0    0    1    0    0    0    0  542    0  ...   0   0   0   0   
Null      7  504  165  200  226    0    0    0    0   31  ...  21  75  52  26   
WT      749   32   50   94   86  640  592  582    0   31  ...  51  10  13  21   
d5       11    4   13   32   10    8    1    0    0  370  ...  21   7   0   6   
d8        2   48  106  108   73    0    0    0    0   78  ...   2  36  15  26   

leiden  41  42  43  44  45  46  
group                           
Base    32   3  31   1  12   1  
Naive    0   0   0   1   0   0  
Null    54   1  29  11   9   0  
WT      29   1  12   8  11   2  
d5       1  88   2  33   0   0  
d8       7   1   4  12   1   1  

[6 rows x 47 columns]
computing neighbors

/scratch/local/20367147/ipykernel_1279247/2632075354.py:25: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  print(obs_df.groupby("group").value_counts().unstack())

    using 'X_pca' with n_pcs = 40
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:01)
running Leiden clustering
    finished: found 27 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running PAGA
    finished: added
    'paga/connectivities', connectivities adjacency (adata.uns)
    'paga/connectivities_tree', connectivities subtree (adata.uns) (0:00:00)
--> added 'pos', the PAGA positions (adata.uns['paga'])

computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:04)

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1216: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning
  color_vector = pd.Categorical(values.map(color_map))
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

# End of Notebook
print("\nNotebook Ends")

Notebook Ends

Neighbor Optimization RPE¶

Initialize Environment¶

Optimize Nearest Neighbors¶

25 PCs, neigbhor 3 to 18¶

30 PCs, neigbhor 3 to 18¶

35 PCs, neigbhor 3 to 18¶

40 PCs, neigbhor 3 to 18¶